rdfs:comment
| - For example, the lawn is wet. But if it rained last night, then it would be unsurprising that the lawn is wet. Therefore, by abductive reasoning, the possibility that it rained last night is reasonable. (But note that Peirce did not remain convinced that a single logical form covers all abduction.) Moreover, abducing rain last night from the observation of the wet lawn can lead to a false conclusion. In this example, dew, lawn sprinklers, or some other process may have resulted in the wet lawn, even in the absence of rain.
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abstract
| - For example, the lawn is wet. But if it rained last night, then it would be unsurprising that the lawn is wet. Therefore, by abductive reasoning, the possibility that it rained last night is reasonable. (But note that Peirce did not remain convinced that a single logical form covers all abduction.) Moreover, abducing rain last night from the observation of the wet lawn can lead to a false conclusion. In this example, dew, lawn sprinklers, or some other process may have resulted in the wet lawn, even in the absence of rain. Peirce argues that good abductive reasoning from P to Q involves not simply a determination that, e.g., Q is sufficient for P, but also that Q is among the most economical explanations for P. Simplification and economy call for the 'leap' of abduction. In abductive reasoning, unlike in deductive reasoning, the premises do not guarantee the conclusion. One can understand abductive reasoning as "inference to the best explanation." The fields of law, computer science, and artificial intelligence research renewed interest in the subject of abduction. Diagnostic expert systems frequently employ abduction.
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